Journal
FUZZY SETS AND SYSTEMS
Volume 161, Issue 13, Pages 1790-1802Publisher
ELSEVIER
DOI: 10.1016/j.fss.2009.11.013
Keywords
Fuzzy clustering; Ensemble classifier; Deflection; Information entropy
Funding
- National Natural Science Foundation of China [90612003]
- Shandong Province, China [2007ZZ17, 2008GG10001015, 2008B0026, J09LG02]
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Ensembles of classifiers can increase the performance of pattern recognition, and have become a hot research topic. High classification accuracy and diversity of the component classifiers are essential to obtain good generalization capability of an ensemble. We review the methods used to learn diverse classifiers, employ fuzzy clustering with deflection to learn the distribution characteristics of the training data, and propose a novel sampling approach to generate training data sets for the component classifiers. Our approach increases the classification accuracy and diversity of the component classifiers. The approach is evaluated using the base classifier c4.5, and the experimental results show that it outperforms Bagging and AdaBoost on almost all the randomly selected 20 benchmark UCI data sets. (C) 2009 Elsevier B.V. All rights reserved.
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